Triple
T9540790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dingolfing-Landau |
E230149
|
entity |
| Predicate | hasAutomotiveIndustryPresence |
P53279
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: true | Statement: [Dingolfing-Landau, hasAutomotiveIndustryPresence, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAutomotiveIndustryPresence Context triple: [Dingolfing-Landau, hasAutomotiveIndustryPresence, true]
-
A.
hasAutomotiveIndustryBrand
Indicates that an entity possesses, is associated with, or operates a specific brand within the automotive industry.
-
B.
containsIndustry
chosen
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
C.
hasIndustrialCompany
Indicates that one entity possesses, controls, or is associated with an industrial company.
-
D.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
-
E.
hasMajorAutomakerHeadquarters
Indicates that a location serves as the primary corporate headquarters for a major automobile manufacturing company.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca847b1b3081908f72bc932c17cc41 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98e695948190ab107fff38c57de7 |
completed | April 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69ccd58bd21881908b860e3ee469af13 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:01 p.m.